OpenClaw Guide 2026: How to Install, Set Up, and Use It
TL;DR
This guide covers the best options for openclaw guide, how to install, set up, and use it. We've tested and ranked each tool based on quality, pricing, and real-world performance. Scroll down for detailed reviews, pricing breakdowns, and our top picks.
OpenClaw became one of the fastest-growing open-source projects in GitHub history, crossing hundreds of thousands of stars in its first months (about 385,000 on GitHub as of August 2026) and arguably defining the "local autonomous agent" category alongside Devin, Claude Code, and Aider. If you've heard the hype but you're not sure what it actually does, who it's for, or how to get started without accidentally letting an AI delete your home directory, this guide covers everything from your first install to the workflows people are actually running in 2026.
The short pitch: OpenClaw is a locally-running, open-source AI agent that takes a frontier LLM (Claude, GPT-5, DeepSeek, or a local model via Ollama) and wraps it in a skills system that lets it execute real actions on your computer and interact with you through messaging apps like Telegram, WhatsApp, Discord, or Slack. Instead of typing into a chat box, you message your agent on Telegram like you'd message a colleague, and it quietly runs tasks in the background, flags interesting things, and reports back. It is to ChatGPT what a personal executive assistant is to a search engine: it does things without being asked.
This guide is for technically comfortable users, people who have Node installed, are comfortable on the command line, and understand what it means to give an AI access to their file system. If that's you, OpenClaw is one of the most powerful free tools in the 2026 AI ecosystem. If it's not, start with Claude Code or ChatGPT Plus with Operator instead.
The bottom line
What it is: A locally-running, open-source AI agent that connects LLMs to your messaging apps, files, and system tools. What it costs: Free (MIT licence), you pay only for LLM API calls, typically $10-50/mo for moderate use. Who it's for: Technically comfortable users who want autonomous AI that acts without being prompted. Not recommended for non-technical users due to real security considerations.
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Subscribe free →What is OpenClaw?
OpenClaw is an autonomous AI agent that runs locally on your computer (Mac, Windows, or Linux) and connects to messaging apps like WhatsApp, Telegram, Discord, and Slack as its interface. Unlike ChatGPT or Claude, which wait for you to type a question, OpenClaw runs continuously in the background, proactively completing tasks, monitoring conditions, and messaging you when it finds something worth flagging.
It uses a skills system (community-expandable through ClawHub, with 54 plugins bundled in the core npm package as of August 2026) to execute actions: run shell commands, read and write files, browse the web, call APIs, and interact with apps. A key differentiator is persistent memory, context stored locally across sessions, making it learn your preferences over time.
How to install OpenClaw
Requirements (checked against the official docs in August 2026): Node 22.22.3+, 24.15+, or 25.9+, with Node 26 recommended. The 22.16 minimum quoted in our earlier version is out of date. You also want at least 16GB RAM and an LLM API key (Claude, OpenAI, or a local model via Ollama). Note that OpenAI has scheduled its legacy GPT-4 variants for retirement on 23 October 2026, so pick a current model when you configure the key.
# Install globally via npm
npm install -g openclaw@latest
# Run the interactive setup wizard
openclaw onboardThe openclaw onboard command walks you through: setting up the local gateway daemon, choosing your AI model, connecting a messaging channel, and installing your first skills. For most users, the Telegram or WhatsApp channel is the recommended starting point.
Connecting your first messaging channel
OpenClaw communicates through messaging apps you already use. To connect Telegram: create a new Telegram bot via @BotFather, copy the bot token, and enter it during openclaw onboard or via openclaw configure --section channels. Once connected, message your bot to start interacting with OpenClaw.
Top skills to install first
The bundled skill library covers the most common use cases. After onboarding, the recommended first skills are: web-search (browse the web and answer questions with current information), file-manager (read, write, and organise local files), scheduler (set up recurring tasks and cron jobs), and github (manage repositories, create PRs, and monitor issues).
Status note, August 2026: we could not confirm any of the four names above against OpenClaw's current bundled inventory, so treat them as illustrative rather than as packages you can install verbatim. The 54 plugins that ship in the core npm package today include browser, memory-core, webhooks, workboard, and provider plugins such as anthropic, openai, ollama, and google. Check the official plugin inventory before you install.
Skills and plugins are installed with different commands. A skill (a SKILL.md instruction pack) installs with openclaw skills install @owner/<slug>, and add --global to make it available to every agent. A plugin installs with openclaw plugins install <source>:<package>, where source is clawhub, npm, git, or a local path. Our earlier version gave the plugin command for skill names, which is the wrong command for that job.
Real use cases people are running
The most popular workflows documented by the community include: automated daily briefings (weather, calendar, news, email summary sent each morning), background monitoring (disk space, server uptime, price trackers), code review and PR management (triggered via GitHub webhooks), and personal research agents that compile reports on topics you're tracking.
The car negotiation story went viral: a user tasked OpenClaw with buying a car, it scraped dealer inventories, filled contact forms, and played dealers against each other over several days, saving thousands.
Security considerations
OpenClaw gives an AI agent significant access to your system. The most important safeguards: run in a sandboxed environment or a dedicated VPS rather than your primary machine, use workspace-level skills rather than global access where possible, and follow the project's own hardening guide for containerised deployments. Correction, August 2026: an earlier version of this guide pointed readers to an "Nvidia NemoClaw" security add-on. We could not find that product on NVIDIA's NeMo product listing or anywhere in OpenClaw's own security documentation, so we have removed the recommendation. What the official docs do cover is full Gateway containerisation plus tool-level sandboxing via agents.defaults.sandbox, which has built-in Docker and Podman backends, exec approval modes with allowlist and ask guardrails, and an openclaw security audit command. The docs are also explicit that OpenClaw assumes a single trusted operator per gateway and is not a hostile multi-tenant security boundary, so run one isolated gateway per tenant if several people need their own agent. The project's own maintainers note it is too risky for users who can't follow command-line instructions.
OpenClaw full review on ToolChase →
Common pitfalls to avoid
Pitfall 1: Running OpenClaw on your main workstation without isolation. An autonomous agent with shell access can accidentally delete files, push broken commits, or exhaust API credits in a loop. The strong recommendation is to run OpenClaw in a dedicated VM, a VPS, or at minimum a Docker container, not on the laptop that holds your unbacked-up photos.
Pitfall 2: Using expensive LLMs for every task. A 20-step agent loop on Claude Opus or GPT-5 can burn $5 in a single task if the agent gets stuck. Use cheaper models (DeepSeek V3, Gemini Flash, Claude Haiku) for routine tasks and reserve the big models for complex reasoning. OpenClaw's model routing lets you configure this per skill.
Pitfall 3: Installing every community skill at once. Each skill expands the action surface the agent can take, and the potential for mistakes. Start with 3-5 essential skills, live with them for a week, and only add more as you find yourself wishing the agent could do something specific.
Pitfall 4: Skipping the approval-required setting. OpenClaw can be configured to require human approval for high-impact actions (file deletes, financial transactions, emails sent, git pushes). For your first month, leave approvals on. Only turn them off for actions you would be comfortable letting a moderately-competent intern perform unsupervised.
Who should use OpenClaw (and who shouldn't)
You should use OpenClaw if: you're technically comfortable, you want a truly autonomous background agent rather than a chat box, you want to keep data local and avoid sending everything to a vendor cloud, you already run a home server or VPS, or you love the tinkering side of AI and want to build custom skills.
You should not use OpenClaw if: you're looking for a polished consumer product (use ChatGPT Plus or Claude Pro), you're not comfortable on the command line, you handle sensitive client or customer data that requires enterprise-grade controls, or you just want a smart chat assistant without the overhead of managing your own agent infrastructure. For those users, ChatGPT Plus with Operator, Claude with Claude Code, or Manus for asynchronous task delivery are better starting points. See OpenClaw alternatives for the full list.
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Take the free quiz →Frequently asked questions
How much does OpenClaw cost to run?
OpenClaw itself is free and open-source. You pay for LLM API costs, typically $10-50/mo for moderate use with Claude or GPT-4. Using local models via Ollama brings the cost to zero beyond hardware.
What's the minimum hardware for OpenClaw?
Node 22.22.3+, 24.15+, or 25.9+ (Node 26 recommended, per the official docs as of August 2026) and 16GB RAM minimum. The recommended setup is a Mac Studio M4 Max or a dedicated Linux server. For single-user personal use, most modern laptops with 16GB RAM work.
Is OpenClaw safe to use?
It is powerful but carries real security risks. Giving an AI agent shell access and file system control on your main computer requires trust in the model's judgment. Sandboxed VPS deployments reduce the risk substantially, as does the official tooling: Docker or Podman sandbox backends, exec approval modes, and the openclaw security audit command. We removed an earlier reference to an "Nvidia NemoClaw" add-on in August 2026 because we could not verify that it exists.
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FAQ
Is OpenClaw free?
Yes. OpenClaw is open source (MIT licensed) and free to run on your own hardware. Cloud inference via the OpenClaw team is free with rate limits and paid for higher throughput. The value proposition is that you can run it locally with no per-token cost, unlike Claude Code or Cursor which require paid subscriptions. You still need a capable machine, typically a modern GPU or an M-series Mac with 32GB+ RAM.
How is OpenClaw different from Claude Code or Devin?
OpenClaw is open source and self-hosted, Claude Code and Devin are proprietary SaaS. OpenClaw gives you full control, privacy and zero marginal cost per task. Claude Code gives you polish, quality and the latest models. Devin gives you autonomous end-to-end agent behaviour, and it is no longer the premium-priced outlier it was: the $500/mo tier is gone, and as of August 2026 devin.ai lists Free, Pro at $20/mo, Max at $200/mo, Teams at $80/mo plus $40/mo per full dev seat, and custom Enterprise pricing. For privacy-sensitive work, OpenClaw. For quality-sensitive work, Claude Code. For autonomy experiments, Devin.
Is OpenClaw safe to use?
OpenClaw is MIT-licensed open source, you can read the code and audit it. Running it locally is as safe as any local tool. The risks: (1) it can execute arbitrary shell commands if you grant it file-system access, so sandbox it. (2) The models it runs are as safe as the underlying Llama or Mistral weights. (3) Community plugins are not vetted, install only from trusted sources. Use a Docker container for production work.
What are the hardware requirements for OpenClaw?
Minimum: 16GB RAM + modest GPU (8GB VRAM) for running 7B models. Recommended: 32GB RAM + 16GB VRAM (RTX 4070 Ti or better) for 13B-70B models. Best: 64GB+ RAM + RTX 4090 / A100 / M4 Max for running the largest models smoothly. Apple Silicon users can run medium-sized models on M3/M4 with 32GB+ unified memory. Without a capable GPU, OpenClaw is painfully slow.
What models does OpenClaw support?
OpenClaw is model-agnostic. Popular choices: Llama 3.1 (8B, 70B), Mistral Large, Qwen 2.5-Coder, DeepSeek-V2 Coder. It also supports connecting to remote APIs (Claude, GPT-5, Gemini) if you want to mix local and cloud. The project ships configs for each supported model. For coding tasks, Qwen 2.5-Coder 32B and DeepSeek Coder V2 are the strongest open models.
Can OpenClaw run on Windows, Mac and Linux?
Yes, all three. Linux has the best GPU driver support (CUDA). Mac runs on Apple Silicon with MLX/Metal acceleration. Windows runs with CUDA drivers for NVIDIA GPUs. Installation is easiest on Linux and Mac; Windows requires WSL2 for smoothest experience. Docker images work across all platforms. Official docs cover setup for each OS.
Does OpenClaw work offline?
Yes, that's one of the main reasons to use it. With local models, OpenClaw runs fully offline after initial model download. Great for secure environments, air-gapped networks, flights, and anywhere internet is unreliable. Cloud connectors (to Claude, GPT-5) obviously require internet but can be optional. Most OpenClaw users run it offline by default.
Is OpenClaw good for production use?
Yes, for specific use cases. It's a good fit for: privacy-sensitive industries (healthcare, legal, finance) that can't send code to third-party APIs, budget-constrained teams that can't afford per-seat SaaS fees, and researchers who need reproducible environments. It's a poor fit for: teams that need the latest model quality (GPT-5 and Claude Opus 5, released 24 July 2026, still lead), polished UX, or immediate bug fixes from vendor support.
What are the best alternatives to OpenClaw?
Cursor ($20/mo) for the best commercial IDE experience. Claude Code (in Claude Pro $20/mo) for the best agentic coding quality. GitHub Copilot ($10/mo) for the cheapest commercial option. Continue.dev + Ollama for a similar local setup. Aider for terminal-based local agent workflows. OpenClaw vs Continue.dev is the most common comparison for local-first devs.
How steep is the learning curve for OpenClaw?
Steeper than commercial tools. Expect 1-2 days to set up your environment, download models, and configure agent settings. Cursor or Copilot is 5 minutes to productive. OpenClaw rewards developers comfortable with the command line, Docker, and Python environments. If you're not already running local LLMs, start with Ollama + Continue.dev before tackling OpenClaw, it's a gentler introduction.
Can I contribute to OpenClaw?
Yes, it's open source on GitHub under MIT licence. Contributions welcome for plugins, model configs, UI improvements, docs and bug fixes. The community is active on Discord and GitHub Issues. Regular contributor rhythm is ~30 PRs per week. Popular first contributions: adding support for a new model, writing docs for your OS, or building a plugin for your editor. Check the CONTRIBUTING.md file before submitting.
Does OpenClaw work with VS Code?
Yes. OpenClaw ships a VS Code extension that connects to a local OpenClaw server. It provides chat, agent mode and inline completions similar to Cursor. The extension is lightweight; heavy lifting happens in the separate OpenClaw server. For Neovim users, there's a community plugin. JetBrains and Sublime support is community-maintained and less polished.